Automate 28-day skin-tracking analysis and generate shareable improvement reports.
npx skills add https://github.com/raydocs/SkinLab --skill effect-tracking
Please help me install this Agent Skill. Skill: effect-tracking Source: https://github.com/raydocs/SkinLab/tree/main/.factory/skills/effect-tracking Command: npx skills add https://github.com/raydocs/SkinLab --skill effect-tracking
This skill enables users to track their skincare journey over a 28-day cycle, automatically analyzing changes with AI and generating a shareable improvement report.
Start a new 28-day tracking session, capture the Day 0 photo, then continue capturing on Days 7, 14, 21, and 28 to generate a final report.
High-intent search queries and answers about installing and using this skill.
Track 28-day skin changes by capturing baseline photos on Day 0, checking in on Days 7, 14, 21, and 28, and letting AI analyze the photos to generate a shareable improvement report.
Automate skin analysis photo comparisons by starting a tracking session that uses an AI service to periodically analyze captured check-in photos and quantify changes over a 28-day cycle.
Yes, skin tracking works with SwiftData by locally storing defined data models for TrackingSession, CheckIn, and TrackingReport to persist your 28-day check-in history and AI analysis results.
Yes, you can share skincare tracking results because the skill generates a comprehensive final synthesis report detailing your improvements and suggested next steps in a shareable format.
To start a 28-day skin tracking session you need to capture a Day 0 baseline photo, after which the workflow guides you through periodic check-ins and AI-driven analyses until Day 28.
Skin tracking uses a 28-day cycle because it maps to the natural skin renewal process, structuring baseline capture and periodic AI photo analyses to accurately quantify improvement over the full cycle.
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